---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-in21k-finetuned-papsmear
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8897058823529411
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# vit-base-patch16-224-in21k-finetuned-papsmear

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co./google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3853
- Accuracy: 0.8897

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log        | 0.9231  | 9    | 1.7589          | 0.2426   |
| 1.7862        | 1.9487  | 19   | 1.5880          | 0.3824   |
| 1.6727        | 2.9744  | 29   | 1.4212          | 0.4265   |
| 1.5102        | 4.0     | 39   | 1.2241          | 0.5809   |
| 1.3247        | 4.9231  | 48   | 1.0906          | 0.6103   |
| 1.1047        | 5.9487  | 58   | 0.9747          | 0.6765   |
| 0.9405        | 6.9744  | 68   | 0.8745          | 0.7426   |
| 0.823         | 8.0     | 78   | 0.7833          | 0.7426   |
| 0.7244        | 8.9231  | 87   | 0.7160          | 0.7794   |
| 0.6367        | 9.9487  | 97   | 0.7328          | 0.7794   |
| 0.5537        | 10.9744 | 107  | 0.6573          | 0.7868   |
| 0.484         | 12.0    | 117  | 0.5988          | 0.8088   |
| 0.4642        | 12.9231 | 126  | 0.6268          | 0.7941   |
| 0.4166        | 13.9487 | 136  | 0.6549          | 0.7794   |
| 0.4106        | 14.9744 | 146  | 0.5330          | 0.8529   |
| 0.3947        | 16.0    | 156  | 0.5134          | 0.8382   |
| 0.3469        | 16.9231 | 165  | 0.5879          | 0.7794   |
| 0.3151        | 17.9487 | 175  | 0.5683          | 0.8382   |
| 0.2946        | 18.9744 | 185  | 0.5383          | 0.8162   |
| 0.2927        | 20.0    | 195  | 0.5682          | 0.8162   |
| 0.2879        | 20.9231 | 204  | 0.4722          | 0.8603   |
| 0.2512        | 21.9487 | 214  | 0.4806          | 0.8456   |
| 0.2633        | 22.9744 | 224  | 0.4713          | 0.8456   |
| 0.2286        | 24.0    | 234  | 0.5167          | 0.8382   |
| 0.2265        | 24.9231 | 243  | 0.3886          | 0.8824   |
| 0.2107        | 25.9487 | 253  | 0.4396          | 0.8676   |
| 0.2044        | 26.9744 | 263  | 0.4734          | 0.8456   |
| 0.1925        | 28.0    | 273  | 0.4606          | 0.8529   |
| 0.1866        | 28.9231 | 282  | 0.5061          | 0.8309   |
| 0.1928        | 29.9487 | 292  | 0.4202          | 0.8824   |
| 0.1907        | 30.9744 | 302  | 0.5120          | 0.8309   |
| 0.1631        | 32.0    | 312  | 0.4165          | 0.8676   |
| 0.1654        | 32.9231 | 321  | 0.4600          | 0.8676   |
| 0.154         | 33.9487 | 331  | 0.3834          | 0.8971   |
| 0.1459        | 34.9744 | 341  | 0.3686          | 0.8897   |
| 0.1452        | 36.0    | 351  | 0.4174          | 0.8676   |
| 0.1548        | 36.9231 | 360  | 0.3791          | 0.9044   |
| 0.1395        | 37.9487 | 370  | 0.4512          | 0.8529   |
| 0.1333        | 38.9744 | 380  | 0.3775          | 0.8897   |
| 0.1236        | 40.0    | 390  | 0.3666          | 0.8971   |
| 0.1236        | 40.9231 | 399  | 0.3892          | 0.8971   |
| 0.1314        | 41.9487 | 409  | 0.3832          | 0.8897   |
| 0.1322        | 42.9744 | 419  | 0.3919          | 0.8824   |
| 0.1156        | 44.0    | 429  | 0.3699          | 0.8971   |
| 0.1222        | 44.9231 | 438  | 0.3828          | 0.8971   |
| 0.1254        | 45.9487 | 448  | 0.3853          | 0.8897   |
| 0.1129        | 46.1538 | 450  | 0.3853          | 0.8897   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1